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FedNoisy: Federated Noisy Label Learning Benchmark

20 June 2023

Papers citing "FedNoisy: Federated Noisy Label Learning Benchmark"

50 / 52 papers shown

Title
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data Alpaslan Gokcen Ali Boyaci FedML 79 0 0 14 May 2025
FNBench: Benchmarking Robust Federated Learning against Noisy Labels Xuefeng Jiang Jia Li Nannan Wu Z. F. Wu Xujing Li Sheng Sun Gang Xu Yansen Wang Qi Li Min Liu FedML 79 3 0 10 May 2025
Federated Learning Client Pruning for Noisy Labels Mahdi Morafah Hojin Chang Chong Chen Bill Lin 90 1 0 11 Nov 2024
Federated Learning with Extremely Noisy Clients via Negative Distillation Yang Lu Lin Chen Yonggang Zhang Yiliang Zhang Bo Han Yiu-ming Cheung Hanzi Wang FedML 88 10 0 20 Dec 2023
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients Zheshun Wu Zenglin Xu Dun Zeng Qifan Wang Jie Liu FedML 84 1 0 11 Oct 2023
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity Nannan Wu Li Yu Xue Jiang Kwang-Ting Cheng Zengqiang Yan FedML 97 37 0 09 May 2023
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection Haobo Zhang Junyuan Hong Fan Dong Steve Drew Liangjie Xue Jiayu Zhou FedML 76 17 0 07 Feb 2023
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data Tailin Zhou Jun Zhang Danny H. K. Tsang FedML 66 60 0 17 Nov 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings Jean Ogier du Terrail Samy Ayed Edwige Cyffers Felix Grimberg Chaoyang He ... Sai Praneeth Karimireddy Marco Lorenzi Giovanni Neglia Marc Tommasi M. Andreux FedML 98 157 0 10 Oct 2022
FLAIR: Federated Learning Annotated Image Repository Congzheng Song Filip Granqvist Kunal Talwar FedML 67 28 0 18 Jul 2022
Communication-Efficient Robust Federated Learning with Noisy Labels Junyi Li Jian Pei Heng Huang FedML 83 18 0 11 Jun 2022
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels Zhuowei Wang Dinesh Manocha Guodong Long Bo Han Jing Jiang FedML 100 19 0 20 May 2022
FedCorr: Multi-Stage Federated Learning for Label Noise Correction Jingyi Xu Zihan Chen Tony Q.S. Quek Kai Fong Ernest Chong FedML 54 89 0 10 Apr 2022
Federated Learning Based on Dynamic Regularization D. A. E. Acar Yue Zhao Ramon Matas Navarro Matthew Mattina P. Whatmough Venkatesh Saligrama FedML 76 781 0 08 Nov 2021
Federated Learning for Open Banking Guodong Long Yue Tan Jing Jiang Chengqi Zhang AIFin FedML 92 275 0 24 Aug 2021
FedLab: A Flexible Federated Learning Framework Dun Zeng Siqi Liang Xiangjing Hu Hui Wang Zenglin Xu FedML 66 115 0 24 Jul 2021
RobustFed: A Truth Inference Approach for Robust Federated Learning Farnaz Tahmasebian Jian Lou Li Xiong FedML 52 23 0 18 Jul 2021
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging Liangqiong Qu N. Balachandar D. Rubin 61 24 0 18 Jul 2021
Federated Noisy Client Learning Huazhu Fu Li Li Bo Han Chengzhong Xu Ling Shao FedML 76 26 0 24 Jun 2021
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space Quande Liu Cheng Chen J. Qin Qi Dou Pheng-Ann Heng OOD FedML 166 440 0 10 Mar 2021
Learning Deep Neural Networks under Agnostic Corrupted Supervision Boyang Liu Mengying Sun Ding Wang P. Tan Jiayu Zhou 65 5 0 12 Feb 2021
Federated Learning on Non-IID Data Silos: An Experimental Study Yue Liu Yiqun Diao Quan Chen Bingsheng He FedML OOD 151 992 0 03 Feb 2021
Robust Federated Learning with Noisy Labels Seunghan Yang Hyoungseob Park Junyoung Byun Changick Kim FedML NoLa 56 80 0 03 Dec 2020
A Principled Approach to Data Valuation for Federated Learning Tianhao Wang Johannes Rausch Ce Zhang R. Jia Basel Alomair FedML TDI 43 193 0 14 Sep 2020
Flower: A Friendly Federated Learning Research Framework Daniel J. Beutel Taner Topal Akhil Mathur Xinchi Qiu Javier Fernandez-Marques ... Lorenzo Sani Kwing Hei Li Titouan Parcollet Pedro Porto Buarque de Gusmão Nicholas D. Lane FedML 136 815 0 28 Jul 2020
FedML: A Research Library and Benchmark for Federated Machine Learning Chaoyang He Songze Li Jinhyun So Xiao Zeng Mi Zhang ... Yang Liu Ramesh Raskar Qiang Yang M. Annavaram Salman Avestimehr FedML 243 577 0 27 Jul 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 111 993 0 16 Jul 2020
Normalized Loss Functions for Deep Learning with Noisy Labels Xingjun Ma Hanxun Huang Yisen Wang Simone Romano S. Erfani James Bailey NoLa 73 445 0 24 Jun 2020
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning Wonyong Jeong Jaehong Yoon Eunho Yang Sung Ju Hwang FedML 59 222 0 22 Jun 2020
DivideMix: Learning with Noisy Labels as Semi-supervised Learning Junnan Li R. Socher Guosheng Lin NoLa 107 1,034 0 18 Feb 2020
Federated Learning with Matched Averaging Hongyi Wang Mikhail Yurochkin Yuekai Sun Dimitris Papailiopoulos Y. Khazaeni FedML 121 1,129 0 15 Feb 2020
FOCUS: Dealing with Label Quality Disparity in Federated Learning Yiqiang Chen Xiaodong Yang Xin Qin Han Yu Biao Chen Zhiqi Shen FedML 55 96 0 29 Jan 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 266 6,285 0 10 Dec 2019
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis Davood Karimi Haoran Dou Simon K. Warfield Ali Gholipour NoLa 95 543 0 05 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 541 42,591 0 03 Dec 2019
Symmetric Cross Entropy for Robust Learning with Noisy Labels Yisen Wang Xingjun Ma Zaiyi Chen Yuan Luo Jinfeng Yi James Bailey NoLa 96 904 0 16 Aug 2019
Bayesian Nonparametric Federated Learning of Neural Networks Mikhail Yurochkin Mayank Agarwal S. Ghosh Kristjan Greenewald T. Hoang Y. Khazaeni FedML 129 730 0 28 May 2019
Unsupervised Label Noise Modeling and Loss Correction Eric Arazo Sanchez Diego Ortego Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 90 615 0 25 Apr 2019
Robust Inference via Generative Classifiers for Handling Noisy Labels Kimin Lee Sukmin Yun Kibok Lee Honglak Lee Yue Liu Jinwoo Shin NoLa 92 139 0 31 Jan 2019
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 158 1,422 0 03 Dec 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Zhilu Zhang M. Sabuncu NoLa 85 2,610 0 20 May 2018
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels Bo Han Quanming Yao Xingrui Yu Gang Niu Miao Xu Weihua Hu Ivor Tsang Masashi Sugiyama NoLa 120 2,078 0 18 Apr 2018
Joint Optimization Framework for Learning with Noisy Labels Daiki Tanaka Daiki Ikami T. Yamasaki Kiyoharu Aizawa NoLa 74 712 0 30 Mar 2018
Robust Loss Functions under Label Noise for Deep Neural Networks Aritra Ghosh Himanshu Kumar P. Sastry NoLa OOD 78 959 0 27 Dec 2017
Deep Learning from Noisy Image Labels with Quality Embedding Jiangchao Yao Jiajie Wang Ivor Tsang Ya Zhang Jun-wei Sun Chengqi Zhang Rui Zhang NoLa 80 121 0 02 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 289 9,803 0 25 Oct 2017
WebVision Database: Visual Learning and Understanding from Web Data Wen Li Limin Wang Wei Li E. Agustsson Luc Van Gool VLM 90 441 0 09 Aug 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 348 4,635 0 10 Nov 2016
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Zhuang Li NoLa 115 1,458 0 13 Sep 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,593 0 17 Feb 2016